DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
Acknowledgement is made of Applicant’s claim amendments on 05/01/2026. The claim amendments are entered. Presently, claims 1-2, 4-11, 13-17 remain pending. Claims 1-2, 4-8, and 10 have been amended and claims 16-17 are newly added.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-2, 4-11, 13-17 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Applicant argues: Guo refers to attributes from multiple images instead of a same content item (pg. 10-12 of remarks)
Examiner response: Examiner respectfully disagrees. Guo teaches that the similarity score can indicate that the compact vectors can refer to the same object (i.e., content item) (Guo para [0084] “In block 414, the discriminator 258 may determine whether the first object in the first image and the second object in the second image is the same object based on the similarity score.”).
Applicant argues: Applicant argues replacing the first attribute through the second neural network can reduce the performance degradation of the neural network (pages 15-16 of remarks).
Examiner response: Examiner respectfully disagrees. Paragraph [0094] of Applicant’s specification states that the generating an attribute by the second neural network to replace the first attribute may help improve the performance of the neural network. While the claims recite that the second attribute is being generated and the first attribute is excluded, the claims do not explicitly recite that the second attribute generated by the second neural network is used as input to train the first neural network. MPEP 2106.04(d)(1) states “If the specification sets forth an improvement in technology, the claim must be evaluated to ensure that the claim itself reflects the disclosed improvement” however, the improvement of using the second attribute to replace the first attribute to train the first neural network is not reflected in the claims. Arguments are not persuasive.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-2, 4-11, 13-17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1 recites:
Step 2A, Prong 1
“obtain a plurality of attribute values for content” (This step is a recitation of a mental process that is practical to perform in the human mind. A human can determine attributes for a content item. (i.e., observation, evaluation, judgement, opinion). See MPEP § 2106.04(a)(2), subsection III.).
“based on obtaining the plurality of attribute values for the content, identify a first attribute value among the plurality of attribute values” (This step is a recitation of a mental process that is practical to perform in the human mind. A human can identify a first attribute among a plurality of attributes. (i.e., observation, evaluation, judgement, opinion). See MPEP § 2106.04(a)(2), subsection III.).
“obtain a second attribute value for the content corresponding to the first attribute value by inputting, to a second neural network model, at least one relevant attribute value related to the first attribute value for the content among the plurality of attribute values” (This step is a recitation of a mental process that is practical to perform in the human mind. A human can determine a second attribute based on a relevant attribute in a plurality of attributes. (i.e., observation, evaluation, judgement, opinion). See MPEP § 2106.04(a)(2), subsection III.).
“based on similarity between the first attribute value for the content and the second attribute value for the content being greater than or equal to a first threshold value, obtain prediction information for the content by inputting, to a first neural network model, one or more attribute values among the plurality of attribute values and the second attribute value excluding the first attribute value” (This step is a recitation of a mental process that is practical to perform in the human mind. A human can determine that the similarity between two attributes are greater than a threshold to obtain prediction information about a content. (i.e., observation, evaluation, judgement, opinion). See MPEP § 2106.04(a)(2), subsection III.).
“based on the similarity between the first attribute value and the second attribute value being less than the first threshold value, obtain the prediction information for the content by inputting the plurality of attribute values including the first attribute value to the first neural network model” (This step is a recitation of a mental process that is practical to perform in the human mind. A human can determine that the similarity between two attributes is less than a threshold to obtain prediction information about a content. (i.e., observation, evaluation, judgement, opinion). See MPEP § 2106.04(a)(2), subsection III.).
Step 2A, Prong 2
“An electronic device comprising: a memory; and at least one processor, comprising processing circuitry, configured, individually and/or collectively” (Using an electronic device, memory, processor, and neural network to make a prediction is mere instructions to apply the exception using a generic computer component. See 2106.05(f).)
“obtain a second attribute value corresponding to the first attribute value by inputting, to a second neural network model, at least one relevant attribute value related to the first attribute value among the plurality of attribute values” (Using a neural network to determine a second attribute are mere instructions to apply the exception using a generic computer component. See 2106.05(f).)
“based on similarity between the first attribute value for the content and the second attribute value for the content being greater than or equal to a first threshold value, obtain prediction information for the content by inputting, to a first neural network model, one or more attribute values among the plurality of attribute values and the second attribute value excluding the first attribute value” (Using a neural network to make a prediction are mere instructions to apply the exception using a generic computer component. See 2106.05(f).)
“based on the similarity between the first attribute value and the second attribute value being less than the first threshold value, obtain the prediction information for the content by inputting the plurality of attribute values including the first attribute value to the first neural network model” (Using a neural network to make a prediction are mere instructions to apply the exception using a generic computer component. See 2106.05(f).)
“train the second neural network model based on the obtained prediction information and thereby improve performance of the first neural network model by generating the second attribute value to replace the first attribute value, wherein the first attribute value is not used for learning of the first neural network model” (mere instructions to apply the exception using a generic computer component. See 2106.05(f).)
Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application.
Step 2B
“An electronic device comprising: a memory; and at least one processor, comprising processing circuitry, configured, individually and/or collectively” (Using an electronic device, memory, processor, and neural network to make a prediction is mere instructions to apply the exception using a generic computer component. See 2106.05(f).)
“obtain a second attribute value corresponding to the first attribute value by inputting, to a second neural network model, at least one relevant attribute value related to the first attribute value among the plurality of attribute values” (Using a neural network to determine a second attribute are mere instructions to apply the exception using a generic computer component. See 2106.05(f).)
“based on similarity between the first attribute value for the content and the second attribute value for the content being greater than or equal to a first threshold value, obtain prediction information for the content by inputting, to a first neural network model, one or more attribute values among the plurality of attribute values and the second attribute value excluding the first attribute value” (Using a neural network to make a prediction are mere instructions to apply the exception using a generic computer component. See 2106.05(f).)
“based on the similarity between the first attribute value and the second attribute value being less than the first threshold value, obtain the prediction information for the content by inputting the plurality of attribute values including the first attribute value to the first neural network model” (Using a neural network to make a prediction are mere instructions to apply the exception using a generic computer component. See 2106.05(f).)
“train the second neural network model based on the obtained prediction information and thereby improve performance of the first neural network model by generating the second attribute value to replace the first attribute value, wherein the first attribute value is not used for learning of the first neural network model” (mere instructions to apply the exception using a generic computer component. See 2106.05(f).)
Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer and insignificant extra-solution activity, which do not provide an inventive concept.
Claim 2 recites:
Step 2A, Prong 1
“obtain a loss value comprising the prediction information obtained through the first neural network model and label information corresponding to the plurality of attribute values” (This step is a recitation of a mental process that is practical to perform in the human mind. A human can determine a loss in a prediction based on a label corresponding to attributes. (i.e., observation, evaluation, judgement, opinion). See MPEP § 2106.04(a)(2), subsection III.).Step 2A, Prong 2
“obtain a loss value comprising the prediction information obtained through the first neural network model and label information corresponding to the plurality of attribute values” (Determining a loss based on a neural network is mere instructions to apply the exception using a generic computer component. See 2106.05(f).)
“train the second neural network model based on the loss value” (Determining a loss based on a neural network is mere instructions to apply the exception using a generic computer component. See 2106.05(f).)
“wherein training of the first neural network model is stopped while the second neural network model is being trained” (linking judicial exception to a field of use. See MPEP 2106.05(h).)
Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application.
Step 2B
“obtain a loss value comprising the prediction information obtained through the first neural network model and label information corresponding to the plurality of attribute values” (Determining a loss based on a neural network is mere instructions to apply the exception using a generic computer component. See 2106.05(f).)
“train the second neural network model based on the loss value” (Determining a loss based on a neural network is mere instructions to apply the exception using a generic computer component. See 2106.05(f).)
“wherein training of the first neural network model is stopped while the second neural network model is being trained” (linking judicial exception to a field of use. See MPEP 2106.05(h).)
Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer and insignificant extra-solution activity, which do not provide an inventive concept.
Claim 4 recites:
Step 2A, Prong 1
“based on at least one of information about a correlation between the plurality of attribute values and information about distribution of each of the plurality of attribute values, identify the at least one relevant attribute value related to the first attribute value among the plurality of attribute values” (This step is a recitation of a mental process that is practical to perform in the human mind. A human can identify a relevant feature based on the similarity and distribution of attributes in their mind. (i.e., observation, evaluation, judgement, opinion). See MPEP § 2106.04(a)(2), subsection III.).
Step 2A, Prong 2 & 2B
The claim does not recite any additional elements
Claim 5 recites:
Step 2A, Prong 1
“obtain first test values corresponding to the first attribute value by inputting, to the second neural network model, each of remaining attribute values other than the first attribute value among the plurality of attribute values,” (This step is a recitation of a mental process that is practical to perform in the human mind. A human can determine a value based on remaining attributes that do not include a first attribute. (i.e., observation, evaluation, judgement, opinion). See MPEP § 2106.04(a)(2), subsection III.).
“identify first test values having similarity that is greater than a specified second threshold value among the first test values,” (This step is a recitation of a mental process that is practical to perform in the human mind. A human can identify test values that have a similarity greater than a threshold. (i.e., observation, evaluation, judgement, opinion). See MPEP § 2106.04(a)(2), subsection III.).
“identify attribute values corresponding to first test values having similarity with the first attribute value greater than or equal to a specified second threshold value as candidate attribute values to identify the relevant attribute value,” (This step is a recitation of a mental process that is practical to perform in the human mind. A human can identify attributes that have a similarity equal to or greater than a threshold in their mind. (i.e., observation, evaluation, judgement, opinion). See MPEP § 2106.04(a)(2), subsection III.).
“and identify the at least one relevant attribute value related to the first attribute value based on the identified candidate attribute values.” (This step is a recitation of a mental process that is practical to perform in the human mind. A human can identify a relevant attribute related to a first attribute and an identified attribute in their mind. (i.e., observation, evaluation, judgement, opinion). See MPEP § 2106.04(a)(2), subsection III.).
Step 2A, Prong 2
“obtain first test values corresponding to the first attribute value by inputting, to the second neural network model, each of remaining attribute values other than the first attribute value among the plurality of attribute values,” (Determining test values using a neural network is mere instructions to apply the exception using a generic computer component. See 2106.05(f).)
Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application.Step 2B
“obtain first test values corresponding to the first attribute value by inputting, to the second neural network model, each of remaining attribute values other than the first attribute value among the plurality of attribute values,” (Determining test values using a neural network is mere instructions to apply the exception using a generic computer component. See 2106.05(f).)
Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer and insignificant extra-solution activity, which do not provide an inventive concept.
Claim 6 recites:
Step 2A, Prong 1
“obtain second test values corresponding to the first attribute value by inputting, to the second neural network model, each of the combinations of the identified candidate attribute values” (This step is a recitation of a mental process that is practical to perform in the human mind. A human can determine test values corresponding to a first attribute based on a combination of candidate attributes in their mind. (i.e., observation, evaluation, judgement, opinion). See MPEP § 2106.04(a)(2), subsection III.).
“identify second test values having similarity with the first attribute value greater than a specified third threshold value among the second test values,” (This step is a recitation of a mental process that is practical to perform in the human mind. A human can identify test values that are similar to a first attribute greater than a threshold in their mind. (i.e., observation, evaluation, judgement, opinion). See MPEP § 2106.04(a)(2), subsection III.).
“identify combinations corresponding to second test values having similarity with the first attribute value greater than or equal to a specified third threshold value as candidate attribute values to identify the relevant attribute value,” (This step is a recitation of a mental process that is practical to perform in the human mind. A human can identify combinations of values that are similar to a first attribute greater than a threshold in their mind. (i.e., observation, evaluation, judgement, opinion). See MPEP § 2106.04(a)(2), subsection III.).
“and identify attribute values included in a combination in which similarity between the second test values and the first attribute value is the highest among the identified candidate combinations as the at least one relevant attribute value related to the first attribute value” (This step is a recitation of a mental process that is practical to perform in the human mind. A human can identify which attribute has the highest similarity. (i.e., observation, evaluation, judgement, opinion). See MPEP § 2106.04(a)(2), subsection III.).
Step 2A, Prong 2
“obtain second test values corresponding to the first attribute value by inputting, to the second neural network model, each of the combinations of the identified candidate attribute values” (Determining test values using a neural network is mere instructions to apply the exception using a generic computer component. See 2106.05(f).)
Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application.Step 2B
“obtain second test values corresponding to the first attribute value by inputting, to the second neural network model, each of the combinations of the identified candidate attribute values” (Determining test values using a neural network is mere instructions to apply the exception using a generic computer component. See 2106.05(f).)
Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer and insignificant extra-solution activity, which do not provide an inventive concept.
Claim 7 recites:
Step 2A, Prong 1
“based on obtaining the plurality of attribute values for the content, identify the first attribute value and the third attribute value among the plurality of attribute values” (This step is a recitation of a mental process that is practical to perform in the human mind. A human can identify a first and second attribute among a plurality of attributes (i.e., observation, evaluation, judgement, opinion). See MPEP § 2106.04(a)(2), subsection III.)
“obtain a fourth attribute value corresponding to the third attribute value by inputting at least one relevant attribute value related to the third attribute value, among the plurality of attribute values, to the second neural network model” (This step is a recitation of a mental process that is practical to perform in the human mind. A human can determine a fourth attribute related to a third attribute based on a relevant attribute correlated with a third attribute (i.e., observation, evaluation, judgement, opinion). See MPEP § 2106.04(a)(2), subsection III.)
“based on similarity between the third attribute value and the fourth attribute value being greater than or equal to a specified fourth threshold value, obtain prediction information for the content by inputting one or more attribute values other than the first attribute value and the third attribute value among the plurality of attribute values, the second attribute value, and the fourth attribute value to the first neural network model” (This step is a recitation of a mental process that is practical to perform in the human mind. A human can make a prediction based on a similarity between a third and fourth attribute being greater than a threshold using a second and fourth attribute but not a first and third attribute (i.e., observation, evaluation, judgement, opinion). See MPEP § 2106.04(a)(2), subsection III.)
Step 2A, Prong 2
“obtain a fourth attribute value corresponding to the third attribute value by inputting at least one relevant attribute value related to the third attribute value, among the plurality of attribute values, to the second neural network model” (Determining a fourth attribute using a neural network is mere instructions to apply the exception using a generic computer component. See 2106.05(f).)
“based on similarity between the third attribute value and the fourth attribute value being greater than or equal to a specified fourth threshold value, obtain prediction information for the content by inputting one or more attribute values other than the first attribute value and the third attribute value among the plurality of attribute values, the second attribute value, and the fourth attribute value to the first neural network model” (Making a prediction using a neural network is mere instructions to apply the exception using a generic computer component. See 2106.05(f).)
Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application.Step 2B
“obtain a fourth attribute value corresponding to the third attribute value by inputting at least one relevant attribute value related to the third attribute value, among the plurality of attribute values, to the second neural network model” (Determining a fourth attribute using a neural network is mere instructions to apply the exception using a generic computer component. See 2106.05(f).)
“based on similarity between the third attribute value and the fourth attribute value being greater than or equal to a specified fourth threshold value, obtain prediction information for the content by inputting one or more attribute values other than the first attribute value and the third attribute value among the plurality of attribute values, the second attribute value, and the fourth attribute value to the first neural network model” (Making a prediction using a neural network is mere instructions to apply the exception using a generic computer component. See 2106.05(f).)
Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer and insignificant extra-solution activity, which do not provide an inventive concept.
Claim 8 recites:
Step 2A, Prong 1
Claim 8 recites at least the abstract idea identified above in claim 1.
Step 2A, Prong 2
“an inputter comprising input circuitry; and an outputter comprising output circuitry;” (Generic computer components. See 2106.05(f).)
“wherein the processor is further configured to: control the outputter to provide the obtained prediction information,” (mere instructions to apply the exception using a generic computer component. See 2106.05(f).)
“receive feedback for the prediction information through the inputter” (insignificant extra-solution activity)
“train the second neural network model based on the received feedback” (mere instructions to apply the exception using a generic computer component. See 2106.05(f).)
Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application.Step 2B
“an inputter comprising input circuitry; and an outputter comprising output circuitry;” (Generic computer components. See 2106.05(f).)
“wherein the processor is further configured to: control the outputter to provide the obtained prediction information,” (mere instructions to apply the exception using a generic computer component. See 2106.05(f).)
“receive feedback for the prediction information through the inputter” (This step appears to be directed to transmitting or receiving information, which is well-understood, routine, and conventional. i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); See MPEP 2106.05 (d) (II).)
“train the second neural network model based on the received feedback” (mere instructions to apply the exception using a generic computer component. See 2106.05(f).)
Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer and insignificant extra-solution activity, which do not provide an inventive concept.
Claim 9 recites:
Step 2A, Prong 1
Claim 9 recites at least the abstract idea identified above in claim 1.
Step 2A, Prong 2
“wherein the first attribute value includes an attribute value not included in learning data for learning of the first neural network or an attribute value having frequency included in the learning data less than a specified fifth threshold value” (linking judicial exception to a field of use. See MPEP 2106.05(h).)
Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application.
Step 2B
“wherein the first attribute value includes an attribute value not included in learning data for learning of the first neural network or an attribute value having frequency included in the learning data less than a specified fifth threshold value” (linking judicial exception to a field of use. See MPEP 2106.05(h).)
Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer and insignificant extra-solution activity, which do not provide an inventive concept.
Claim 10 recites:
Step 2A, Prong 1
See rejection of claim 1. Same rationale applies.
Claim 11 recites:
Step 2A, Prong 1
See rejection of claim 2. Same rationale applies.
Claim 13 recites:
Step 2A, Prong 1
See rejection of claim 4. Same rationale applies.
Claim 14 recites:
Step 2A, Prong 1
See rejection of claim 5. Same rationale applies.
Claim 15 recites:
Step 2A, Prong 1
See rejection of claim 6. Same rationale applies.
Claim 16 recites:
Step 2A, Prong 1
Claim 9 recites at least the abstract idea identified above in claim 1.
Step 2A, Prong 2
“wherein the first attribute value comprises an attribute value not used for learning by the first neural network model” (linking judicial exception to a field of use. See MPEP 2106.05(h).)
Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application.
Step 2B
“wherein the first attribute value comprises an attribute value not used for learning by the first neural network model” (linking judicial exception to a field of use. See MPEP 2106.05(h).)
Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer and insignificant extra-solution activity, which do not provide an inventive concept.
Claim 17 recites:
Step 2A, Prong 1
See rejection of claim 16. Same rationale applies.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 4, 7-8, 10, 13, and 16-17 are rejected under 35 U.S.C. 103 as being unpatentable over Guo et al. (US-20190220675-A1) in view of Hazard et al. (US-20210312307-A1) and Kowalski et al. (US-20210335029-A1).
Regarding Claim 1,
Guo (US 20190220675 A1) teaches an electronic device comprising:
a memory; and
at least one processor, comprising processing circuitry, configured, individually and/or collectively, to:
obtain a plurality of attribute values for content; based on obtaining the plurality of attribute values for the content, identify a first attribute value among the plurality of attribute values (figure 4A; para [0055] “For example, the compact representation generator 254 may map the initial feature vector representing the detected object to a compact feature vector representing the detected object. In some embodiments, the compact feature vector may comprise a fewer number of modality features (and thus, having a lower feature dimension and smaller data size) as compared to the corresponding initial feature vector.” The compact feature vector having at least a first attribute is determined from an initial feature vector. Also see figure 7 which shows at least a first attribute. Object (i.e., content).),
obtain a second attribute value for the content corresponding to the first attribute value by inputting, to a second neural network model, at least one relevant attribute value related to the first attribute value among the plurality of attribute values (figure 4A; para [0055] figure 4A shows a first and second compact generator as described in para [0065] and each produce their respective compact feature vectors.),
Guo does not explicitly disclose
based on similarity between the first attribute value for the content and the second attribute value for the content being greater than or equal to a first threshold value, obtain prediction information for the content by inputting, to a first neural network model, one or more attribute values among the plurality of attribute values and the second attribute value excluding the first attribute value,
based on the similarity between the first attribute value and the second attribute value being less than the first threshold value, obtain the prediction information for the content by inputting the plurality of attribute values including the first attribute value to the first neural network model, and
train the second neural network model based on the obtained prediction information and thereby improve performance of the first neural network model by generating the second attribute value to replace the first attribute value, wherein the first attribute value is not used for learning of the first neural network model.
However, Hazard (US 20210312307 A1) teaches
based on similarity between the first attribute value for the content and the second attribute value for the content being greater than or equal to a first threshold value, obtain prediction information for the content by inputting, to a first neural network model, one or more attribute values among the plurality of attribute values and the second attribute value excluding the first attribute value (para [0024] “As an example of the importance of synthetic data differing from the training data, it may be useful to have the synthetic data not contain identical data cases as the original training data or even data cases that meet certain similarity conditions with (e.g., being overly “similar” or “close” to) original training data cases. As such, in the event that synthetic data is identical or too similar to existing training data, the synthetic data case may be modified (e.g., resampled) and retested, or discarded.” Synthetic feature is discarded if too similar. Para [0031]),
based on the similarity between the first attribute value and the second attribute value being less than the first threshold value (para [0104] “In some embodiments, the dropping and replacement of feature values may continue until the case is within a threshold distance of a case in the training data. For example, if a case is below a certain threshold distance to a case in the training data, the dropping and replacement of feature values may be terminated. This may be beneficial when it is the case that having a close distance between the synthetic data case and a case in the training data means that the synthetic data case is sufficiently similar to cases in the training data. In some embodiments, the generated synthetic data may be compared against at least a portion of the existing training data, and a determination may be made whether to keep the synthetic data case based on the distance of the synthetic data case to one or more elements of in the existing training data.” The synthetic feature value is dropped (i.e., excluded) if the similarity to the training data meets or exceeds a threshold. Synthetic feature is not dropped if it is below the similarity threshold.), obtain the prediction information for the content by inputting the plurality of attribute values including the first attribute value to the first neural network model (para [0078] “For example, not depicted in FIG. 1A, a computer-based reasoning model may be trained based on the synthetic data case(s) (and/or other sets of synthetic data cases, the training cases, and or a combination of such cases, or a combination of (sub)sets of such cases, etc.), and that model may be used to control a controllable system.”), and
Guo and Hazard are analogous because they are both directed to the field machine learning.
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the machine learning model of Guo with the similarity threshold of Hazard.
Doing so would allow for ensuring the synthetic data not contain identical data cases as the original training data or even data cases that meet certain similarity conditions with (e.g., being overly “similar” or “close” to) original training data cases (Hazard para [0024]).
However, Kowalski (US 20210335029 A1) teaches
train the second neural network model based on the obtained prediction information and thereby improve performance of the first neural network model by generating the second attribute value to replace the first attribute value, wherein the first attribute value is not used for learning of the first neural network model (para [0087] “One-shot learning by fine tuning comprises pre-training the encoder and the decoders (using the first and second stages of FIG. 9 or only the second stage of FIG. 9) and then training again using real images and with a loss function that encourages the neural renderer to reduce an identity gap between a face depicted in the real image and in the output image. It is unexpectedly found that one-shot learning by fine tuning is effective. One-shot learning modifies the embeddings and the whole decoder and it is surprising that control of the output image is still possible through the factorized embeddings even after one-shot learning by fine tuning has been done.” The decoder (i.e. first model) is pretrained based on embeddings (i.e., first attributes) from synthetic images. Later, the decoder later inputs an embedding (i.e., second attribute) produced by the real encoder as shown in figure 9 but not the first embedding. Para [0081] At the second stage, the encoder (i.e., second model) is updated through back propagation improving the encoder’s performance.)
Guo and Kowalski are analogous because they are both directed to the field of neural networks.
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the neural network of Guo with the one-shot learning of Kowalski.
Doing so would allow for fine-tuning the neural network to minimize the difference between the output and target output (Kowalski para [0087]).
Regarding Claim 4,
Guo, Hazard and Kowalski teach the electronic device of claim 1. Guo further teaches wherein the processor is further configured to, based on at least one of information about a correlation between the plurality of attribute values and information about distribution of each of the plurality of attribute values, identify the at least one relevant attribute value related to the first attribute value among the plurality of attribute values (para [0074] “As discussed above, the similarity score between the first compact feature vector 422a of the first object and the n.sup.th compact feature vector 422n of the second object is 70%, rather than 100% as indicated by the predetermined target output. Because of the feedback difference of 30% between the similarity score and the predetermined target output, the compact representation generator 254 may determine that other modality features (e.g., the context features, the viewpoint features, etc.) may be more determinative than the texture features and the color features (and thus, more efficient and distinguishable in representing the detected objects) if the initial feature representations of the detected objects includes the texture features and the color features within these particular ranges of feature values.” Similarity scores represents a correlation between the plurality of distributions. The range of values indicates a distribution. Context features can be more relevant thus given more weight than other features.).
Regarding Claim 7,
Guo, Hazard and Kowalski teach the electronic device of claim 1. Guo further teaches wherein the processor is further configured to:
based on obtaining the plurality of attribute values for the content, identify the first attribute value and a third attribute value among the plurality of attribute values (figure 4A; para [0055] “For example, the compact representation generator 254 may map the initial feature vector representing the detected object to a compact feature vector representing the detected object. In some embodiments, the compact feature vector may comprise a fewer number of modality features (and thus, having a lower feature dimension and smaller data size) as compared to the corresponding initial feature vector.” The compact feature vector has at least a first and second attribute which is determined from an initial feature vector. Also see figure 7.),
obtain a fourth attribute value corresponding to the third attribute value by inputting at least one relevant attribute value related to the third attribute value, among the plurality of attribute values, to the second neural network model (figure 4A; para [0055] figure 4A shows a first and second compact generator as described in para [0065] and each produce their respective compact feature vectors.), and
based on similarity between the third attribute value and the fourth attribute value being greater than or equal to a specified fourth threshold value, obtain prediction information for the content by inputting one or more attribute values (para [0080] “ In some embodiments, the discriminator 258 may determine whether the similarity score between the first compact representation 422a of the first object and the n.sup.th compact representation 422n of the second object satisfies a predetermined score threshold (e.g., more than 50%). Responsive to determining that the similarity score between the first compact representation 422a of the first object and the n.sup.th compact representation 422n of the second object satisfies the predetermined score threshold, the discriminator 258 may determine that the first object in the first image and the second object in the second image represent the same object.”) other than the first attribute value and the third attribute value among the plurality of attribute values, the second attribute value, and the fourth attribute value to the first neural network model (para [0075] “As a result, when the compact representation generators 254 process similar initial feature vectors 420 (e.g., the initial feature vectors 420 having the texture features and/or the color features of approximately the same feature values), the texture features and/or the color features are likely filtered out from the initial feature vectors 420 to generate the corresponding compact feature vectors 422 of the detected objects”).
Regarding Claim 8,
Guo, Hazard and Kowalski teach the electronic device of claim 1. Guo further teaches further comprising:
an inputter comprising input circuitry; and an outputter comprising output circuitry (para [0095] “Input/output or I/O devices (including but not limited to keyboards, displays, pointing devices, etc.) can be coupled to the system either directly or through intervening I/O controllers.”);
wherein the processor is further configured to:
control the outputter to provide the obtained prediction information, receive feedback for the prediction information through the inputter (para [0072] “The similarity scorer 256 may compare the similarity score to the predetermined target output, and therefore determine the feedback difference between the similarity score and the predetermined target output to be 30%.”), and
train the second neural network model based on the received feedback (para [0072] “In some embodiments, the similarity scorer 256 may provide the feedback difference between the similarity score and the predetermined target output to the compact representation generators 254a . . . 254n for training the model.”).
Regarding Claim 10,
Claim 10 is the method corresponding to the device of claim 1. Claim 10 is substantially similar to claim 1 and is rejected on the same grounds.
Regarding Claim 13,
Claim 13 is the method corresponding to the device of claim 4. Claim 13 is substantially similar to claim 4 and is rejected on the same grounds.
Regarding Claim 16,
Guo, Hazard, and Kowalski teach the electronic device of claim 1. Hazard further teaches wherein the first attribute value comprises an attribute value not used for learning by the first neural network model (para [0024] “As an example of the importance of synthetic data differing from the training data, it may be useful to have the synthetic data not contain identical data cases as the original training data or even data cases that meet certain similarity conditions with (e.g., being overly “similar” or “close” to) original training data cases. As such, in the event that synthetic data is identical or too similar to existing training data, the synthetic data case may be modified (e.g., resampled) and retested, or discarded.”).
Guo and Hazard are analogous because they are both directed to the field machine learning.
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the machine learning model of Guo with the similarity threshold of Hazard.
Doing so would allow for ensuring the synthetic data not contain identical data cases as the original training data or even data cases that meet certain similarity conditions with (e.g., being overly “similar” or “close” to) original training data cases (Hazard para [0024]).
Regarding Claim 17,
Claim 17 is the method corresponding to the device of claim 16. Claim 17 is substantially similar to claim 16 and is rejected on the same grounds.
Claims 2 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Guo/Hazard/Kowalski, as applied above, and further in view of Kim et al. (US-20220284921-A1).
Regarding Claim 2,
Guo, Hazard and Kowalski teach the electronic device of claim 1.
Kowalski further teaches
and wherein training of the first neural network model is stopped while the second neural network model is being trained (para [0079] “With reference to FIG. 9 a first stage 900 involves omitting the real data encoder 904 and randomly generating 906 embeddings of real images. During the first stage the synthetic data encoder and the decoder are trained using backpropagation 908 and using synthetic images.” The real data encoder is stopped while the decoder is trained.)
Guo, Hazard and Kowalski do not explicitly disclose
wherein the processor is further configured to: obtain a loss value comprising the prediction information obtained through the first neural network model and label information corresponding to the plurality of attribute values, and train the second neural network model based on the loss value,
However, Kim (US 20220284921 A1) teaches
wherein the processor is further configured to: obtain a loss value comprising the prediction information obtained through the first neural network model and label information corresponding to the plurality of attribute values (para [0053] “A label of a voice section or a non-voice section is assigned to each frame time of the input signal.”), and train the second neural network model based on the loss value (para [0057] “In the self-supervised learning according to the present embodiment, the first neural network and the second neural network are trained on the basis of a loss function using the synchronized correlation coefficient as a positive sample and the unsynchronized correlation coefficient as a negative sample. By using such a loss function, the first neural network and the second neural network are trained such that the synchronized correlation coefficient increases and the unsynchronized correlation coefficient decreases, that is, to reinforce discrimination for voice and non-voice of the acoustic feature and the non-acoustic feature.”),
Guo, Hazard, Kowalski, and Kim are analogous because they are directed towards comparing feature values between two neural networks.
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the neural network of Guo and Kowalski with the loss function of Kim.
Doing so would allow for updating the learning parameters of the neural network according to a contrastive loss in accordance to any optimization method (Kim para [0091]).
Regarding Claim 11,
Claim 11 is the method corresponding to the device of claim 2. Claim 11 is substantially similar to claim 2 and is rejected on the same grounds.
Claims 5-6, 9, and 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Guo/Hazard/Kowalski, as applied above, and further in view of Kormilitsin et al. (US-20230334360-A1).
Regarding Claim 5,
Guo, Hazard and Kowalski teach the electronic device of claim 1.
Guo, Hazard and Kowalski do not explicitly disclose
wherein the processor is further configured to:
obtain first test values corresponding to the first attribute value by inputting, to the second neural network model, each of remaining attribute values other than the first attribute value among the plurality of attribute values,
identify first test values having similarity that is greater than a specified second threshold value among the first test values,
identify attribute values corresponding to first test values having similarity with the first attribute value greater than or equal to a specified second threshold value as candidate attribute values to identify the relevant attribute value, and
identify the at least one relevant attribute value related to the first attribute value based on the identified candidate attribute values.
However, Kormilitsin (US 20230334360 A1) teaches
obtain first test values corresponding to the first attribute value by inputting, to the second neural network model, each of remaining attribute values other than the first attribute value among the plurality of attribute values (para [0061] “FIG. 8 shows the test data 704 and target data 702 being inputted to the trained prediction model 708 to obtain a second performance measure 810(1) that quantifies the performance of the prediction model 708 in the absence of the first feature f.sub.1. However, instead of training a new model with one fewer feature, which would incur significant computational resources, the test data 704(1) for the first feature f.sub.1 is randomized (see randomization 802) to create randomized test data 804(1) that is inputted to the trained prediction model 708. This randomization is equivalent to replacing the test data 704(1) with noise. The second performance measure 810(1) is therefore equivalent to the first performance measure 710 except that the impact of the first feature f.sub.1 has been essentially excluded.”),
identify first test values having similarity that is greater than a specified second threshold value among the first test values (para [0043] “ =A high convergence score (e.g., above a threshold) may indicate that the updated bucket ranking 418 is so similar to the initial bucket ranking 402 that an additional iteration is unlikely to yield significant additional changes to bucket rank (i.e., the method 400 has converged).” para [0052] “high convergence score may indicate that the updated candidate-feature ranking 518 is so similar to the initial candidate-feature ranking 502 that an additional iteration is unlikely to yield significant additional changes to candidate-feature rank (i.e., the method 400 has converged).”),
identify attribute values corresponding to first test values having similarity with the first attribute value greater than or equal to a specified second threshold value as candidate attribute values to identify the relevant attribute value (para [0043] “ =A high convergence score (e.g., above a threshold) may indicate that the updated bucket ranking 418 is so similar to the initial bucket ranking 402 that an additional iteration is unlikely to yield significant additional changes to bucket rank (i.e., the method 400 has converged).” para [0052] “high convergence score may indicate that the updated candidate-feature ranking 518 is so similar to the initial candidate-feature ranking 502 that an additional iteration is unlikely to yield significant additional changes to candidate-feature rank (i.e., the method 400 has converged).”), and
identify the at least one relevant attribute value related to the first attribute value based on the identified candidate attribute values (para [0028] “The first candidate feature f.sub.1 has the highest score s.sub.1.sup.(0) and therefore may be referred to as the most-relevant candidate feature. Similarly, the second candidate feature f.sub.2 has the second highest score s.sub.2.sup.(0) and therefore may be referred to as the second most-relevant candidate feature.”).
Guo, Hazard, Kowalski, and Kormilitsin are analogous because they are directed to the field of neural networks.
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the neural network of Guo, Hazard and Kowalski with the feature selection of Kormilitsin.
Doing so would allow reducing the number of non-relevant features to improve computation resource usage and the model’s accuracy (Kormilitsin para [0008]).
Regarding Claim 6,
Guo, Hazard, Kowalski, and Kormilitsin teach the electronic device of claim 5. Kormilitsin further teaches wherein the processor is further configured to:
obtain second test values corresponding to the first attribute value by inputting, to the second neural network model (para [0060] “In FIG. 7, test data 704 associated with each of the p.sub.1 candidate features 102 in the first bucket 110(1) are inputted to a trained prediction model 708.”), each of the combinations of the identified candidate attribute values,
identify second test values having similarity with the first attribute value greater than a specified third threshold value among the second test values (Abs. “The Swiss tournament is advantageous when there are so many candidate feature variables that it is computationally infeasible to test all combinations of these candidates.”),
identify combinations corresponding to second test values having similarity with the first attribute value greater than or equal to a specified third threshold value as candidate attribute values to identify the relevant attribute value (para [0043] “ =A high convergence score (e.g., above a threshold) may indicate that the updated bucket ranking 418 is so similar to the initial bucket ranking 402 that an additional iteration is unlikely to yield significant additional changes to bucket rank (i.e., the method 400 has converged).” para [0052] “high convergence score may indicate that the updated candidate-feature ranking 518 is so similar to the initial candidate-feature ranking 502 that an additional iteration is unlikely to yield significant additional changes to candidate-feature rank (i.e., the method 400 has converged).”), and
identify attribute values included in a combination in which similarity between the second test values and the first attribute value is the highest among the identified candidate combinations as the at least one relevant attribute value related to the first attribute value (para [0028] “The first candidate feature f.sub.1 has the highest score s.sub.1.sup.(0) and therefore may be referred to as the most-relevant candidate feature. Similarly, the second candidate feature f.sub.2 has the second highest score s.sub.2.sup.(0) and therefore may be referred to as the second most-relevant candidate feature.”).
Guo, Hazard, Kowalski, and Kormilitsin are analogous because they are directed to the field of neural networks.
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the neural network of Guo and Kowalski with the feature selection of Kormilitsin.
Doing so would allow reducing the number of non-relevant features to improve computation resource usage and the model’s accuracy (Kormilitsin para [0008])
Regarding Claim 9,
Guo, Hazard, and Kowalski teach the electronic device of claim 1.
wherein the first attribute value includes an attribute value not included in learning data for learning of the first neural network or an attribute value having frequency included in the learning data less than a specified fifth threshold value.
However, Kormilitsin (US 20230334360 A1) teaches
wherein the first attribute value includes an attribute value not included in learning data for learning of the first neural network or an attribute value having frequency included in the learning data less than a specified fifth threshold value (para [0061] “The second performance measure 810(1) is therefore equivalent to the first performance measure 710 except that the impact of the first feature f.sub.1 has been essentially excluded.”),
Guo, Hazard, Kowalski, and Kormilitsin are analogous because they are directed to the field of neural networks.
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the neural network of Guo and Kowalski with the feature selection of Kormilitsin.
Doing so would allow reducing the number of non-relevant features to improve computation resource usage and the model’s accuracy (Kormilitsin para [0008]).
Regarding Claim 14,
Claim 14 is the method corresponding to the device of claim 5. Claim 14 is substantially similar to claim 5 and is rejected on the same grounds.
Regarding Claim 15,
Claim 15 is the method corresponding to the device of claim 6. Claim 15 is substantially similar to claim 6 and is rejected on the same grounds.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/H.N./Examiner, Art Unit 2121
/Li B. Zhen/Supervisory Patent Examiner, Art Unit 2121
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